Analytics Gurukul

Data Analytics

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Machine Learning

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Machine Learning Course Modules :

    • INTRODUCTION TO MACHINE LEARNING
    • The origins of machine learning
    • Uses and abuses of machine learning
    • Machine learning successes
    • The limits of machine learning
    • Machine learning ethics
    • How machines learn
    • Data storage
    • Abstraction
    • Generalization
    • Evaluation
    • Machine learning in practice
    • Types of input data
    • Types of machine learning algorithms
    • Matching input data to algorithms

     

     

     

     

     

    • Regression Algorithms

     

    • The most popular regression algorithms are:
    • Ordinary Least Squares Regression (OLSR)
    • Linear Regression
    • Logistic Regression
    • Stepwise Regression

     

     

     

     

    • Instance-based Algorithms

     

    • The most popular instance-based algorithms are:
    • k-Nearest Neighbor (kNN)
    • Learning Vector Quantization (LVQ)
    • Self-Organizing Map (SOM)
    • Locally Weighted Learning (LWL)
    • Support Vector Machines (SVM)

     

     

    • Regularization Algorithms

     

    • The most popular regularization algorithms are:
    • Ridge Regression
    • Least Absolute Shrinkage and Selection Operator (LASSO)
    • Elastic Net

     

     

    • Decision Tree Algorithms

     

    • The most popular decision tree algorithms are:
    • Classification and Regression Tree (CART)
    • Iterative Dichotomiser 3 (ID3)
    • 5 and C5.0 (different versions of a powerful approach)
    • Chi-squared Automatic Interaction Detection (CHAID)
    • Decision Stump
    • M5
    • Conditional Decision Trees

     

     

     

    • Bayesian Algorithms

     

    • The most popular Bayesian algorithms are:
    • Naive Bayes
    • Gaussian Naive Bayes
    • Multinomial Naive Bayes
    • Averaged One-Dependence Estimators (AODE)
    • Bayesian Belief Network (BBN)
    • Bayesian Network (BN)

     

     

     

    • Clustering Algorithms

     

    • The most popular clustering algorithms are:
    • k-Means
    • k-Medians
    • Expectation Maximisation (EM)
    • Hierarchical Clustering
    • Divide and Conquer – Classification Using Decision Trees and Rules
    • Understanding decision trees
    • Divide and conquer
    • The decision tree algorithm
    • Choosing the best split
    • Pruning the decision tree
    • Example – identifying risky bank loans using C5.0 decision trees

     

    • Forecasting Numeric Data – Regression Methods
    • Understanding regression
    • Simple linear regression
    • Ordinary least squares estimation
    • Correlations
    • Multiple linear regression
    • Example – predicting medical expenses using linear regression

     

    • Black Box Methods – Neural Networks and Support Vector Machines
    • Understanding neural networks
    • From biological to artificial neurons
    • Activation functions
    • Network topology
    • The number of layers
    • The direction of information travel
    • The number of nodes in each layer
    • Training neural networks with backpropagation
    • Example – Modeling the strength of concrete with ANNs

     

    • Finding Groups of Data – Clustering with k-means
    • Understanding clustering
    • Clustering as a machine learning task
    • The k-means clustering algorithm
    • Using distance to assign and update clusters
    • Choosing the appropriate number of clusters
    • Example – finding teen market segments using k-means clustering

     

    • Finding Patterns – Market Basket Analysis Using Association Rules
    • Understanding association rules
    • The Apriori algorithm for association rule learning
    • Measuring rule interest – support and confidence
    • Building a set of rules with the Apriori principle
    • Example – identifying frequently purchased groceries with
    • Association rules

Duration 24 Hours

Master the Art of SQL Server

Effective communication is at the core of leadership

Leading with Vision and Purpose

Our coaching focuses on developing a clear vision and purpose

We are making our students masters in every aspects Whether it is communication skills, Data Visualization or so on.

Effective Data Collection and Statistical Analysis is the core strength of Data Analytics

Data Visualization

Creating visual representations of data to communicate complex findings effectively. Scientists use tools like charts, graphs

Communication Skills:

Effectively communicating findings and insights to non-technical stakeholders, such as business leaders or decision-makers.

Continuous Learning

Staying updated on the latest developments in data science, machine learning, and related fields.

Optimization and Tuning

Fine-tuning machine learning models to improve their performance, often through hyperparameter tuning